
DIA-NN 2.6.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jun 10 2026 15:42:45
Current date and time: Fri Jul 24 00:59:19 2026
Logical CPU cores: 128
/opt/diann-2.6.0/diann-linux --lib out-DIANN_libA/WU2.6.0_nomods_report-lib.predicted.speclib --fasta input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta --reannotate --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw --threads 24 --qvalue 0.01 --cut  --min-pep-len 7 --max-pep-len 30 --min-pr-charge 2 --max-pr-charge 4 --min-pr-mz 380 --max-pr-mz 980 --min-fr-mz 200 --max-fr-mz 1800 --missed-cleavages 0 --verbose 1 --met-excision --unimod4 --rt-profiling --matrices --pg-level 1 --reanalyse --gen-spec-lib --out-lib out-DIANN_quantB/WU2.6.0_nomods_report-lib.parquet --out out-DIANN_quantB/WU2.6.0_nomods_report.parquet --temp temp-DIANN_quantB 

Library precursors will be reannotated using the FASTA database
Thread number set to 24
Output will be filtered at 0.01 FDR
Min peptide length set to 7
Max peptide length set to 30
Min precursor charge set to 2
Max precursor charge set to 4
Min precursor m/z set to 380
Max precursor m/z set to 980
Min fragment m/z set to 200
Max fragment m/z set to 1800
Maximum number of missed cleavages set to 0
N-terminal methionine excision enabled
Cysteine carbamidomethylation enabled as a fixed modification
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Precursor/protein x samples expression level matrices will be saved along with the main report
Implicit protein grouping: protein names; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
MBR enabled; .quant files will only be saved to disk during the first pass
A spectral library will be generated
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only

6 files will be processed
[0:00] Loading spectral library out-DIANN_libA/WU2.6.0_nomods_report-lib.predicted.speclib
[0:04] Library annotated with sequence database(s): input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:05] Spectral library loaded: 2654746 protein isoforms, 2654663 protein groups and 5042797 precursors in 2707883 elution groups (targets and decoys).
[0:05] Loading FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[1:26] Reannotating library precursors with information from the FASTA database
[1:53] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[1:53] 5042797 precursors generated
[1:57] Gene names missing for some isoforms
[1:57] Library contains 2654746 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[2:04] Initialising library

First pass: generating a spectral library from DIA data

[2:21] File #1/6
[2:21] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[2:37] Pre-processing...
[2:38] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 5038238 precursors in range
[2:39] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[3:03] RT window set to 1.2575
[3:03] Peak width: 2.812
[3:03] Scan window radius set to 6
[3:03] Recommended MS1 mass accuracy setting: 2.2 ppm
[3:23] Optimised mass accuracy: 6 ppm
[3:32] Main search
[4:19] Removing low confidence identifications
[4:28] Removing interfering precursors
[4:38] Training neural networks on 274641 target and 232574 decoy PSMs
[5:31] Number of IDs at 0.01 FDR: 65615
[5:31] Calculating protein q-values
[5:32] Number of proteins identified at 1% FDR: 60979 (precursor-level), 60533 (protein-level) (inference performed using proteotypic peptides only)
[5:32] Quantification
[5:34] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_raw.quant

[5:34] File #2/6
[5:34] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[5:47] Pre-processing...
[5:48] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[5:49] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[6:12] RT window set to 1.25526
[6:12] Recommended MS1 mass accuracy setting: 2.3 ppm
[6:18] Main search
[7:04] Removing low confidence identifications
[7:13] Removing interfering precursors
[7:22] Training neural networks on 276195 target and 229809 decoy PSMs
[8:15] Number of IDs at 0.01 FDR: 67137
[8:15] Calculating protein q-values
[8:16] Number of proteins identified at 1% FDR: 62308 (precursor-level), 61642 (protein-level) (inference performed using proteotypic peptides only)
[8:17] Quantification
[8:19] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_raw.quant

[8:19] File #3/6
[8:19] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[8:31] Pre-processing...
[8:33] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[8:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[8:56] RT window set to 1.28905
[8:56] Recommended MS1 mass accuracy setting: 2.1 ppm
[9:02] Main search
[9:50] Removing low confidence identifications
[10:00] Removing interfering precursors
[10:10] Training neural networks on 283023 target and 238468 decoy PSMs
[11:05] Number of IDs at 0.01 FDR: 67671
[11:05] Calculating protein q-values
[11:06] Number of proteins identified at 1% FDR: 62808 (precursor-level), 61825 (protein-level) (inference performed using proteotypic peptides only)
[11:07] Quantification
[11:09] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_raw.quant

[11:09] File #4/6
[11:09] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[11:23] Pre-processing...
[11:24] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[11:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[11:43] RT window set to 1.35423
[11:43] Recommended MS1 mass accuracy setting: 2.6 ppm
[11:50] Main search
[12:39] Removing low confidence identifications
[12:49] Removing interfering precursors
[12:58] Training neural networks on 283638 target and 238156 decoy PSMs
[13:52] Number of IDs at 0.01 FDR: 71842
[13:52] Calculating protein q-values
[13:53] Number of proteins identified at 1% FDR: 66462 (precursor-level), 65979 (protein-level) (inference performed using proteotypic peptides only)
[13:53] Quantification
[13:55] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_raw.quant

[13:55] File #5/6
[13:55] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[14:09] Pre-processing...
[14:10] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[14:10] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[14:29] RT window set to 1.30108
[14:29] Recommended MS1 mass accuracy setting: 2 ppm
[14:35] Main search
[15:23] Removing low confidence identifications
[15:33] Removing interfering precursors
[15:43] Training neural networks on 287092 target and 241425 decoy PSMs
[16:43] Number of IDs at 0.01 FDR: 70654
[16:44] Calculating protein q-values
[16:45] Number of proteins identified at 1% FDR: 65471 (precursor-level), 64826 (protein-level) (inference performed using proteotypic peptides only)
[16:45] Quantification
[16:47] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_raw.quant

[16:47] File #6/6
[16:47] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[17:00] Pre-processing...
[17:01] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[17:01] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[17:19] RT window set to 1.34046
[17:19] Recommended MS1 mass accuracy setting: 2.6 ppm
[17:26] Main search
[18:14] Removing low confidence identifications
[18:24] Removing interfering precursors
[18:33] Training neural networks on 291545 target and 245275 decoy PSMs
[19:29] Number of IDs at 0.01 FDR: 71713
[19:29] Calculating protein q-values
[19:30] Number of proteins identified at 1% FDR: 66390 (precursor-level), 65739 (protein-level) (inference performed using proteotypic peptides only)
[19:30] Quantification
[19:32] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_raw.quant

[19:32] Cross-run analysis
[19:32] Reading quantification information: 6 files
[19:45] Target precursors at 1% global q-value: 86532
[19:45] Quantifying peptides
[20:11] Assembling protein groups
[20:17] Quantifying proteins
[20:19] Calculating q-values for protein and gene groups
[20:20] Calculating global q-values for protein and gene groups
[20:21] Protein groups with global q-value <= 0.01: 78410
[20:24] Compressed report saved to out-DIANN_quantB/WU2.6.0_nomods_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[20:24] Saving precursor levels matrix
[20:24] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.6.0_nomods_report-first-pass.pr_matrix.tsv.
[20:24] Manifest saved to out-DIANN_quantB/WU2.6.0_nomods_report-first-pass.manifest.txt
[20:24] Stats report saved to out-DIANN_quantB/WU2.6.0_nomods_report-first-pass.stats.tsv
[20:24] Generating spectral library:
[20:26] 106186 target and 5981 decoy precursors saved
[20:26] Spectral library saved to out-DIANN_quantB/WU2.6.0_nomods_report-lib.parquet

[20:33] Loading spectral library out-DIANN_quantB/WU2.6.0_nomods_report-lib.parquet
[20:35] Spectral library loaded: 103005 protein isoforms, 102763 protein groups and 112167 precursors in 103599 elution groups (targets and decoys).
[20:35] Loading protein annotations from FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[20:58] Annotating library proteins with information from the FASTA database
[20:59] Gene names missing for some isoforms
[20:59] Library contains 103005 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[20:59] Initialising library
[21:00] Saving the library to out-DIANN_quantB/WU2.6.0_nomods_report-lib.parquet.skyline.speclib


Second pass: using the newly created spectral library to reanalyse the data

[21:00] File #1/6
[21:00] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[21:11] Pre-processing...
[21:12] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 106186 precursors in range
[21:12] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:13] RT window set to 0.431175
[21:13] Recommended MS1 mass accuracy setting: 2.2 ppm
[21:13] Main search
[21:15] Removing low confidence identifications
[21:17] Removing interfering precursors
[21:18] Training neural networks on 98745 target and 51365 decoy PSMs
[21:32] Number of IDs at 0.01 FDR: 76446
[21:32] Calculating protein q-values
[21:32] Number of proteins identified at 1% FDR: 70347 (precursor-level), 71043 (protein-level) (inference performed using proteotypic peptides only)
[21:32] Quantification

[21:33] File #2/6
[21:33] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[21:46] Pre-processing...
[21:47] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106186 precursors in range
[21:47] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:47] RT window set to 0.440141
[21:47] Recommended MS1 mass accuracy setting: 2 ppm
[21:47] Main search
[21:49] Removing low confidence identifications
[21:51] Removing interfering precursors
[21:52] Training neural networks on 97910 target and 50891 decoy PSMs
[22:06] Number of IDs at 0.01 FDR: 75992
[22:06] Calculating protein q-values
[22:06] Number of proteins identified at 1% FDR: 69959 (precursor-level), 70106 (protein-level) (inference performed using proteotypic peptides only)
[22:06] Quantification

[22:07] File #3/6
[22:07] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[22:20] Pre-processing...
[22:20] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106186 precursors in range
[22:20] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[22:21] RT window set to 0.444615
[22:21] Recommended MS1 mass accuracy setting: 2.4 ppm
[22:21] Main search
[22:23] Removing low confidence identifications
[22:25] Removing interfering precursors
[22:26] Training neural networks on 99028 target and 51721 decoy PSMs
[22:40] Number of IDs at 0.01 FDR: 77860
[22:40] Calculating protein q-values
[22:40] Number of proteins identified at 1% FDR: 71628 (precursor-level), 71890 (protein-level) (inference performed using proteotypic peptides only)
[22:40] Quantification

[22:42] File #4/6
[22:42] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[22:55] Pre-processing...
[22:56] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106186 precursors in range
[22:56] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[22:56] RT window set to 0.455364
[22:56] Recommended MS1 mass accuracy setting: 2.5 ppm
[22:56] Main search
[22:58] Removing low confidence identifications
[23:00] Removing interfering precursors
[23:01] Training neural networks on 100275 target and 52203 decoy PSMs
[23:15] Number of IDs at 0.01 FDR: 80171
[23:15] Calculating protein q-values
[23:15] Number of proteins identified at 1% FDR: 73682 (precursor-level), 73970 (protein-level) (inference performed using proteotypic peptides only)
[23:15] Quantification

[23:17] File #5/6
[23:17] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[23:31] Pre-processing...
[23:32] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106186 precursors in range
[23:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[23:33] RT window set to 0.450536
[23:33] Recommended MS1 mass accuracy setting: 2.4 ppm
[23:33] Main search
[23:35] Removing low confidence identifications
[23:37] Removing interfering precursors
[23:37] Training neural networks on 99815 target and 52027 decoy PSMs
[23:52] Number of IDs at 0.01 FDR: 79139
[23:52] Calculating protein q-values
[23:52] Number of proteins identified at 1% FDR: 72789 (precursor-level), 73175 (protein-level) (inference performed using proteotypic peptides only)
[23:52] Quantification

[23:53] File #6/6
[23:53] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[24:06] Pre-processing...
[24:06] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106186 precursors in range
[24:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[24:07] RT window set to 0.448009
[24:07] Recommended MS1 mass accuracy setting: 2.3 ppm
[24:07] Main search
[24:09] Removing low confidence identifications
[24:11] Removing interfering precursors
[24:12] Training neural networks on 100023 target and 51753 decoy PSMs
[24:26] Number of IDs at 0.01 FDR: 80145
[24:26] Calculating protein q-values
[24:27] Number of proteins identified at 1% FDR: 73703 (precursor-level), 74142 (protein-level) (inference performed using proteotypic peptides only)
[24:27] Quantification

[24:28] Cross-run analysis
[24:28] Reading quantification information: 6 files
[24:30] Target precursors at 1% global q-value: 85285
[24:30] Quantifying peptides
[25:18] Quantification parameters: 0.358717, 0.00165961, 0.00159426, 0.0136937, 0.254076, 0.155342, 0.245422, 0.225498, 0.184843, 0.0145561, 0.0503061, 0.0478427, 0.393601, 0.0516022, 0.0668955, 0.0124508
[25:29] Quantifying proteins
[25:29] Calculating q-values for protein and gene groups
[25:29] Calculating global q-values for protein and gene groups
[25:29] Protein groups with global q-value <= 0.01: 78477
[25:32] Compressed report saved to out-DIANN_quantB/WU2.6.0_nomods_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[25:32] Saving precursor levels matrix
[25:32] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.6.0_nomods_report.pr_matrix.tsv.
[25:32] Saving protein group levels matrix
[25:32] Protein groups matrix saved to out-DIANN_quantB/WU2.6.0_nomods_report.pg_matrix.tsv.
[25:32] Saving gene group levels matrix
[25:32] Gene groups matrix saved to out-DIANN_quantB/WU2.6.0_nomods_report.gg_matrix.tsv.
[25:32] Saving unique genes levels matrix
[25:32] Unique genes matrix saved to out-DIANN_quantB/WU2.6.0_nomods_report.unique_genes_matrix.tsv.
[25:32] Manifest saved to out-DIANN_quantB/WU2.6.0_nomods_report.manifest.txt
[25:32] Stats report saved to out-DIANN_quantB/WU2.6.0_nomods_report.stats.tsv

